Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

2019 ◽  
Vol 12 (4) ◽  
pp. 153-170 ◽  
Author(s):  
Guefrouchi Ryma ◽  
Kholladi Mohamed-Khireddine
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ontology Mapping ◽  
Hill Climbing ◽  
Computational Time ◽  
Mutation Operator ◽  
Mapping Process ◽  
The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

2021 ◽  
pp. 260-278
Author(s):  
Guefrouchi Ryma ◽  
Kholladi Mohamed-Khireddine
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ontology Mapping ◽  
Hill Climbing ◽  
Computational Time ◽  
Mutation Operator ◽  
Mapping Process ◽  
The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

The Optimization Method to Transfer Bus Routes for Rail Transit Terminal Station

2012 ◽  
Vol 253-255 ◽  
pp. 1869-1875
Author(s):  
Sheng Zhang ◽  
Wu Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Hill Climbing ◽  
Rail Transit ◽  
Hill Climbing Algorithm ◽  
Variation Rate ◽  
Encoding Method ◽  
The Hill ◽  
Terminal Station

The optimization model is framed with a goal to minimize overall consumption of travel time for passengers. A variety of constrains are considered, including time, capacity, stop number, profit and so on. According to the features of the model, the hill-climbing algorithm is adopted to obtain the initial solution, which reduces the time of optimization. Meanwhile, direct order encoding method, namely node method, is introduced for encoding, construct a Hybrid Genetic Algorithm for the solution. The results show that adapter value is more steady and the model result is preferable when the variation rate is increased while the number of iteration is decreased.

Smooth gait optimization of a fish robot using the genetic-hill climbing algorithm

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 257-278 ◽  
Author(s):  
Tuong Quan Vo ◽  
Hyoung Seok Kim ◽  
Byung Ryong Lee
Keyword(s):  
Genetic Algorithm ◽  
Hill Climbing ◽  
Optimal Parameters ◽  
Fish Robot ◽  
Hill Climbing Algorithm ◽  
Smooth Motion ◽  
Simulation Results ◽  
The Hill ◽  
Gait Optimization ◽  
Input Torque

SUMMARYThis paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.

Firepower Assignment of Ship-to-Air Missile System

2014 ◽  
Vol 905 ◽  
pp. 702-705
Author(s):  
Yong Hong Lu ◽  
Ji Hua Dou ◽  
Xing Bao Yang ◽  
Chuan Wei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Decision Support ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Hill Climbing ◽  
Unconstrained Optimization Problem ◽  
Provide Decision Support ◽  
Missile System ◽  
Standard Genetic Algorithm

Hybrid genetic algorithm has been proposed in this paper, which is proposed by combining standard genetic algorithm with hill climbing to solve the unconstrained optimization problem, which can get global optimization results of the firepower assignment, and provide decision support for the firepower assignment.

Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning

2013 ◽  
Vol 846-847 ◽  
pp. 1189-1196
Author(s):  
Ping Chuan He ◽  
Shu Ling Dai
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Virtual Environments ◽  
Real Time ◽  
Hill Climbing ◽  
Mutation Operator ◽  
Hill Climbing Algorithm ◽  
Area Of Interest ◽  
Time Planning ◽  
Neighborhood Mutation

This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.

scholarly journals Non-Linear Threshold Algorithm for the Redundancy Optimization of Multi-State Systems

2020 ◽  
Vol 6 (1) ◽  
pp. 416-441
Author(s):  
Nabil Nahas ◽  
Mustapha Nourelfath
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Optimization Problem ◽  
Function Technique ◽  
Computational Time ◽  
System Availability ◽  
Linear Threshold ◽  
Binary State ◽  
Non Linear ◽  
Redundancy Optimization

To improve system performance, redundancy is widely used in different kinds of industrial applications such as power systems, aerospace, electronic, telecommunications and manufacturing systems. Designing high performant systems which meet customer requirements with a minimum cost is a challenging task in these industries. This paper develops an efficient approach for the redundancy optimization problem of series-parallel structures modeled as multi-state systems. To reach the target system availability, redundancies are used for components among a list of products available in the market. Each component is characterized by its own availability, cost and performance. The goal is to minimize the total cost under a system availability constraint. Discrete levels of performance are considered for the system and its components. The extreme values of such performance levels correspond to perfect functioning and complete failure. A piecewise cumulative load curve represents consumer demand. System availability corresponds to the aptitude to fulfill this demand. The multi-state system availability evaluation uses the universal moment generating function technique. The proposed optimization algorithm is based on the non-linear threshold accepting metaheuristic, while using a self-adjusting penalty guided strategy. The obtained results demonstrate the approach efficiency for solving the redundancy optimization problem of multi-state systems. Its effectiveness is also tested using the classical redundancy optimization problem of binary-state systems. The algorithm is evaluated by comparison to the best known methods. For multi-state systems, it is compared to genetic algorithm and tabu search. For binary-state systems, it is compared to genetic algorithm, tabu search, ant colony optimization and harmony search. The obtained results demonstrate that the proposed approach outperforms these state-of-the-art benchmark methods in finding, for all considered instances, a high-quality solution in a minimum computational time.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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